Standard deviation in numpy
Question:
Here is my code:
import numpy as np
print(np.std(np.array([0,1])))
it produces 0.5
I am confident that this is incorrect. What am I doing wrong?
Answers:
By default, numpy.std returns the population standard deviation, in which case np.std([0,1]) is correctly reported to be 0.5. If you are looking for the sample standard deviation, you can supply an optional ddof parameter to std():
>>> np.std([0, 1], ddof=1)
0.70710678118654757
ddof modifies the divisor of the sum of the squares of the samples-minus-mean. The divisor is N - ddof, where the default ddof is 0 as you can see from your result.
Here is my code:
import numpy as np
print(np.std(np.array([0,1])))
it produces 0.5
I am confident that this is incorrect. What am I doing wrong?
By default, numpy.std returns the population standard deviation, in which case np.std([0,1]) is correctly reported to be 0.5. If you are looking for the sample standard deviation, you can supply an optional ddof parameter to std():
>>> np.std([0, 1], ddof=1)
0.70710678118654757
ddof modifies the divisor of the sum of the squares of the samples-minus-mean. The divisor is N - ddof, where the default ddof is 0 as you can see from your result.